The process of object state tracking has been accomplished for many years in a myriad of different ways. State tracking implies that some qualities of an object's geometry relative to a sensing device are being followed and estimated. These qualities are estimated by sensing the generated/reflected energy emissions of the object. Qualities of relative geometry which are tracked include range and/or bearing and/or the respective derivatives from a sensor to the energy source. The expressions state tracking and tracking are often used synonymously in the literature and will be used as such within this document. Early tracking methods often utilized single sensors. To achieve improved tracking accuracy these single sensors were upgraded or replaced with sensors having improved accuracy. Current methods continue to primarily utilize single sensors, although a trend is developing toward mixed mode and multi-sensor systems to overcome the limitations of single sensor systems.
Mixed mode systems utilize different types of sensors such as combined Radio Frequency (RF) and optic sensors collocated upon the same platform. Mixed mode systems are generally utilized when one sensor type complements the capabilities of another sensor type. One sensor type, for example, might have long range detection capability for initial tracking, and another collocated sensor type which has better but range limited accuracy is utilized to provide improved short range tracking. An example of a mixed mode multiple sensor system is U.S. Pat. No. 3,630,079, issued to Hughes.
Multi-sensor systems are utilized to overcome several limitations of single sensor systems. Multiple sensors provide an increasing quantity of available measurements as additional sensors are utilized. A greater number of measurements from multiple collocated sensors, for example, is combined to improve the statistics of tracking system estimates. Additionally, single sensor systems encounter significantly decreased accuracy when tracked objects are in poor relative geometry with the sensor. Multiple geometrically distributed sensors can significantly relieve this problem by viewing the object from different geometric perspectives. Another limitation of single sensor systems is that they are unable to provide information about the relative orientation of multiple bodies, whereas multiple sensor systems have this capability.
The field of spatially distributed multi-sensor tracking is an emerging one, having its major roots beginning around 1980 with developments sponsored by MIT Lincoln Labs. The problems addressed in this field are typically so highly constrained that results are usually not reusable in different multi-sensor tracking situations. Prior art systems, for example, are typically constrained with sensor array formations whereby sensors are permanently fixed at well known (a priori) relative locations and/or orientations. FIG. 1 depicts a typical prior art sensor platform arrangement. Sensors are arranged in an array grid having well known and often equal spacings between sensor elements, i.e. r.sub.x and r.sub.y known. Position vectors between sensor platforms are typically either directly measured with distance measuring equipment, or inferred through the use of an external absolute coordinate determination system such as any navigation system or Global Positioning System (GPS). The relative orientation between the coordinate frames in which pairs of sensor elements function is also typically well known, often identical, and not allowed to change dynamically. Sensors utilized in prior art multi-sensor systems, for example, are very often located upon the same rigid body. Additionally, these sensor arrays are not allowed to experience Own-Body motion or relative motion, and three dimensional problems are often approximated with substantially inaccurate two dimensional models. Prior art multi-sensor tracking methods also typically do not have the flexibility to utilize any combination of range, bearing, and respective derivative information as such information is available. Most major prior art developments are related to either distributed acoustic sensors or distributed ground based radars. Examples include Mati Wax and Thomas Kailath, "Decentralized Processing in Sensor Arrays" published in IEEE Trans. Acous. Speech Sig. Proc., ASSP-33, October 1985 pp. 1123-1128 and Cantrell, B.H., and A. Grindley, "Multiple Site Radar Tracking System" published in Proc. IEEE Int. Conf., April 1980 pp. 348-354.
A typical prior art distributed multi-sensor data fusion information flow diagram is shown in FIG. 2. The first process represented by Block I is to estimate relative sensor positions and alignments. A common prior art example is to align cooperative ground based radars with magnetic or true north. Alignment information is passed to Block V where it is stored for future use. The Measurement process, Block II, provides sensor data measurements of various sensed objects from the radar sites (local nodes) to a central processing agent via Block III, the Communication process. At the central node, the Object Association and Tracking process, Block IV, associates sensor data with common targets and updates object track filters as required. Results are passed to Block V, the Earth Coordinate Mapping and Fusing process, whereby fusion estimates are generated in a common coordinate frame, such a coordinate frame typically being earth coordinates. The fusion estimates are then passed to the Application Interface process, Block VI, which makes the estimates available to the application.
There are many different fusion system architectures which can be implemented to optimize performance under the given multi-sensor tracking system constraints. Examples of fusion system architectures include hierarchical, centralized tracking, and sensor level tracking. Sensor level tracking systems form object tracks at the sensor level. Centralized tracking systems gather sensor data at a single node and all tracking and fusion processing takes place at the central level, or central node. Hierarchical architectures combine the sensor data from groups of local nodes at an intermediate level. Intermediate level nodes feed higher level nodes until possibly reaching a central level. Any node where data processing takes place is generally referred to in the literature as an agent node. Any node where data from multiple sensors is combined (fused) is termed a fusion agent node or fusion node. The combination of a sensing device and a capability for communications with agent nodes is generally referred to as a local node. A local node which is also an agent node is sometimes additionally referred to as a local agent node.
A closely related area is that of multiple object track association. Developments in this area are concerned with associating a set of multiple objects tracked by a sensor with the set of objects tracked by another sensor. Objects appearing to have identical trajectories and falling within a confidence contour (gate) are determined to be common to each set of tracked objects. Early work in this area was concerned the problem of handing off a tracked object from one ground based radar to another. Multiple object track association has more recently received amplified attention due to programs sponsored by the U.S. Army and the Strategic Defense Initiative Organization (SDIO) for analysis of extended threat clouds. Much of the scholastic research in this area is occurring at the University of Connecticut Department of Electrical and Systems Engineering. Examples of work in this area include Blackman, S.S., "Multiple Target Tracking with Radar Applications" published by Artech House, Dedham, Ma 1986 and Bar-Shalom, Y., and T.E. Fortmann, "Tracking and Data Association" published by Academic Press, New York, 1988.
A research area just now receiving attention is concerned with a process termed registration. Registration is the process of determining the relative orientation of one sensor to that of cooperating sensors. The prior art typically does not consider the case of dynamic relative sensor orientations. Cooperative sensors in prior art multi-sensor systems, for example, are typically not located upon different platforms having relative Degrees of Freedom. A representative example of the prior art is one that determines the relative orientation of earth fixed cooperative sensors, a specific example being multiple cooperative ground based radar sites. Prior art techniques for accomplishing the registration process are typically restricted to determining bias offsets about only a single coordinate axis, such as determining the azimuth offset of cooperative ground based radar sites. This is accomplished through various forms of stochastic filtering, including a model of the geometry of multiple radar sites and the tracks of mutually tracked aircraft. An example of efforts in the area of sensor registration is Fischer, W.L., C.E. Muehe, and A.G. Cameron, "Registration Errors in a Netted Air Surveillance System", MIT Lincoln Laboratory Technical Note 1980-40, Sep. 2, 1980 AD-AO93691.
Examples of other patented multi-sensor tracking systems are U.S. Pat. Nos. 4,916,455 issued to Bent et al, 4,976,619 issued to Carlson, and 4,853,863 issued to Cohen et al. These systems utilize cooperative sensors having no relative motion at precalibrated relative positions. The patents to Bent et al and Carlson accomplish position tracking utilizing range-only triangulation whereby the sensor platform orientation is not required or estimated. The patent to Cohen et al uses arrays composed of three non-colinear sensors having known relative positions and orientations which are located upon a 6 Degree of Freedom (6DOF) platform. A different 6DOF platform has three non-colinear emitters at known positions. Geometric relationships are utilized to determine the relative orientations of the two platforms.